If there’s a mask in your model, it’ll be propagated layer-by-layer and eventually applied to the loss. So if you’re padding and masking the sequences in a correct way, the loss on the padding placeholders would be ignored. Some Details: It’s a bit involved to explain the whole process, so I’ll just break it down … Read more
This would not be a standard approach to deal with unbalanced data. Nor do I think it would be really justified – you would be significantly changing the distributions of your classes, where the smaller class is now much less variable. The larger class would have rich variation, the smaller would be many similar images … Read more
As you are trying to use function decorator in TF 2.0, please enable run function eagerly by using below line after importing TensorFlow: tf.config.experimental_run_functions_eagerly(True) Since the above is deprecated(no longer experimental?), please use the following instead: tf.config.run_functions_eagerly(True) If you want to know more do refer to this link.
You’re right to say that kernel_size defines the size of the sliding window. The filters parameters is just how many different windows you will have. (All of them with the same length, which is kernel_size). How many different results or channels you want to produce. When you use filters=100 and kernel_size=4, you are creating 100 … Read more
The key is to first do .get_layer on the Model object, then do another .get_layer on that specifying the specific vgg16 layer, THEN do .output: layer_output = model.get_layer(‘vgg16’).get_layer(‘block3_conv1’).output
since Keras 2.0 metrics f1, precision, and recall have been removed. The solution is to use a custom metric function: from keras import backend as K def f1(y_true, y_pred): def recall(y_true, y_pred): “””Recall metric. Only computes a batch-wise average of recall. Computes the recall, a metric for multi-label classification of how many relevant items are … Read more
Td;lr GlobalMaxPooling1D for temporal data takes the max vector over the steps dimension. So a tensor with shape [10, 4, 10] becomes a tensor with shape [10, 10] after global pooling. MaxPooling1D takes the max over the steps too but constrained to a pool_size for each stride. So a [10, 4, 10] tensor with pooling_size=2 … Read more
Cheking with some tests, I got to the following conclusion, which is according to the documentation and to Nassim’s answer: First, there isn’t a single state in a layer, but one state per sample in the batch. There are batch_size parallel states in such a layer. Stateful=False In a stateful=False case, all the states are … Read more
There are two types of modules – keras tensorflow.keras Here we need to use tensorflow.keras You need to import Adam (With Capital A) from tensorflow – Keras ( Not only Keras). from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers import Adam # – Works from tensorflow.keras.optimizers import adam # – Does not work from keras.optimizers import Adam … Read more
The following commands solved the problem for me pip install pydot pip install pydotplus sudo apt-get install graphviz